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With the ubiquitous nature of WiFi devices and WiFi networks around us, which continuously transmit and receive high bandwidth data, these transmission channels can be used as an effective sensing medium for detecting movements. With the use of Machine Learning, we develop a model using the features of the channel state information to train and predict these movements accurately. This paper proposes a demonstration harnessing the low-cost WiFi-based IoT for the detection of movements. Such a setup will facilitate the usage of movements like hand gestures to carry out common tasks such as changing the television channel, changing slides during a presentation, no-controller wireless gaming, etc. We achieve an F1 score of 0.99 with the latency of 45.75ms.

From the point of view of a software engineer, having safe and optimal controllers for real life systems like cyber physical systems is a crucial requirement before deployment. Given the mathematical model of these systems along with their specifications, model checkers can be used to synthesize controllers for them.The given work proposes novel approaches for making controller analysis easier by using machine learning to represent the controllers synthesized by model checkers in a succinct manner, while also incorporating the domain knowledge of the system. It also proposes the implementation of a visualization tool which will be integrated into existing model checkers. A lucid controller representation along with a tool to visualize it will help the software engineer debug and monitor the system much more efficiently.